Model card

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+library_name: peft
+tags:
+- lora
+- sft
+- dpo
+- knowledge-distillation
+- fine-tuning
+- it-support
+---
+
+# Model Adaptation Book — companion models
+
+Trained artifacts for the book *LLM Customization and Fine-Tuning: Adaptation,
+Distillation, and Alignment* (Manning). Code:
+https://github.com/bahree/ModelAdaptationBook
+
+All are adaptations of `Qwen/Qwen3-4B-Instruct-2507` on a real IT-support
+dataset: Stack Exchange IT Q&A (Super User, Ask Ubuntu, Server Fault;
+CC-BY-SA-4.0) plus a small Databricks Dolly slice (CC-BY-SA-3.0) for
+general-capability retention. Each chapter's artifact is a **subfolder**, so you
+can follow along on any machine (including Apple Silicon) by pulling a trained
+model and running inference/eval, without training it yourself.
+
+| Subfolder | Chapter | What | Base |
+|---|---|---|---|
+| `ch5-lora` | 5 | LoRA adapter | Qwen3-4B-Instruct-2507 |
+| `ch6-sft` | 6 | full SFT model (standalone) | (full fine-tune) |
+| `ch7-distilled` | 7 | distilled student (LoRA) | Qwen3-4B-Instruct-2507 |
+| `ch8-dpo` | 8 | full DPO model (standalone) | (full fine-tune) |
+| `ch8-dpo-lora` | 8 | LoRA-DPO adapter (single-card path) | `ch6-sft` |
+
+Load a full model:
+
+```python
+from transformers import AutoModelForCausalLM
+m = AutoModelForCausalLM.from_pretrained("bahree/ModelAdaptationBook", subfolder="ch6-sft")
+```
+
+Load an adapter (on its base):
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
+m = PeftModel.from_pretrained(base, "bahree/ModelAdaptationBook", subfolder="ch5-lora")
+```
+
+**Training** these needs a CUDA 24 GB+ GPU (and the Ch8 full DPO uses multiple
+GPUs; the `ch8-dpo-lora` adapter is the single-card alternative). **Inference
+and evaluation** fit a single smaller GPU or Apple Silicon (MPS). See the book
+repo for exact commands, datasets, and full attribution.